Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations52413
Missing cells115934
Missing cells (%)13.8%
Duplicate rows1371
Duplicate rows (%)2.6%
Total size in memory8.8 MiB
Average record size in memory176.3 B

Variable types

Numeric14
Categorical2

Alerts

14618_FERM0101.DO_2_PV has constant value "0.0"Constant
Dataset has 1371 (2.6%) duplicate rowsDuplicates
14618_FERM0101.PUMP_1_PV is highly imbalanced (> 99.9%)Imbalance
14618_FERM0101.Agitation_PV has 4319 (8.2%) missing valuesMissing
14618_FERM0101.Air_Sparge_PV has 4319 (8.2%) missing valuesMissing
14618_FERM0101.Biocontainer_Pressure_PV has 4317 (8.2%) missing valuesMissing
14618_FERM0101.DO_1_PV has 4319 (8.2%) missing valuesMissing
14618_FERM0101.DO_2_PV has 51162 (97.6%) missing valuesMissing
14618_FERM0101.Gas_Overlay_PV has 4318 (8.2%) missing valuesMissing
14618_FERM0101.Load_Cell_Net_PV has 4317 (8.2%) missing valuesMissing
14618_FERM0101.pH_1_PV has 4317 (8.2%) missing valuesMissing
14618_FERM0101.pH_2_PV has 4317 (8.2%) missing valuesMissing
14618_FERM0101.PUMP_1_PV has 4319 (8.2%) missing valuesMissing
14618_FERM0101.PUMP_1_TOTAL has 4317 (8.2%) missing valuesMissing
14618_FERM0101.PUMP_2_PV has 4319 (8.2%) missing valuesMissing
14618_FERM0101.PUMP_2_TOTAL has 4318 (8.2%) missing valuesMissing
14618_FERM0101.Single_Use_DO_PV has 4319 (8.2%) missing valuesMissing
14618_FERM0101.Single_Use_pH_PV has 4319 (8.2%) missing valuesMissing
14618_FERM0101.Temperatura_PV has 4318 (8.2%) missing valuesMissing
14618_FERM0101.PUMP_2_PV is highly skewed (γ1 = 113.0348869)Skewed
14618_FERM0101.Agitation_PV has 25737 (49.1%) zerosZeros
14618_FERM0101.Air_Sparge_PV has 47007 (89.7%) zerosZeros
14618_FERM0101.DO_1_PV has 43568 (83.1%) zerosZeros
14618_FERM0101.Gas_Overlay_PV has 22256 (42.5%) zerosZeros
14618_FERM0101.Load_Cell_Net_PV has 985 (1.9%) zerosZeros
14618_FERM0101.PUMP_1_TOTAL has 7595 (14.5%) zerosZeros
14618_FERM0101.PUMP_2_PV has 47837 (91.3%) zerosZeros
14618_FERM0101.PUMP_2_TOTAL has 25441 (48.5%) zerosZeros

Reproduction

Analysis started2024-09-29 18:20:46.436741
Analysis finished2024-09-29 18:21:01.021237
Duration14.58 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

14618_FERM0101.Agitation_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct685
Distinct (%)1.4%
Missing4319
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean21.319701
Minimum0
Maximum80
Zeros25737
Zeros (%)49.1%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:21:01.068348image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q344
95-th percentile72
Maximum80
Range80
Interquartile range (IQR)44

Descriptive statistics

Standard deviation27.653088
Coefficient of variation (CV)1.2970674
Kurtosis-0.71846222
Mean21.319701
Median Absolute Deviation (MAD)0
Skewness0.93124719
Sum1025349.7
Variance764.6933
MonotonicityNot monotonic
2024-09-29T20:21:01.144747image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25737
49.1%
20 8131
 
15.5%
72 7897
 
15.1%
44 2734
 
5.2%
56 285
 
0.5%
40 191
 
0.4%
48 162
 
0.3%
69.75 146
 
0.3%
69.71999512 131
 
0.2%
38.46400146 129
 
0.2%
Other values (675) 2551
 
4.9%
(Missing) 4319
 
8.2%
ValueCountFrequency (%)
0 25737
49.1%
20 8131
 
15.5%
20.08859372 1
 
< 0.1%
20.18091561 1
 
< 0.1%
20.36940037 1
 
< 0.1%
20.55766634 1
 
< 0.1%
20.61520405 1
 
< 0.1%
20.74596573 1
 
< 0.1%
20.93433997 1
 
< 0.1%
21.08880422 1
 
< 0.1%
ValueCountFrequency (%)
80 1
 
< 0.1%
72 7897
15.1%
71.99993896 1
 
< 0.1%
71.99987183 1
 
< 0.1%
71.99980751 1
 
< 0.1%
71.99974365 1
 
< 0.1%
71.99967938 1
 
< 0.1%
71.99961548 1
 
< 0.1%
71.99954834 1
 
< 0.1%
71.99948405 1
 
< 0.1%

14618_FERM0101.Air_Sparge_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct1088
Distinct (%)2.3%
Missing4319
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean0.17821932
Minimum0
Maximum16.003441
Zeros47007
Zeros (%)89.7%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:21:01.217902image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum16.003441
Range16.003441
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4626509
Coefficient of variation (CV)8.2070279
Kurtosis89.57233
Mean0.17821932
Median Absolute Deviation (MAD)0
Skewness9.2947051
Sum8571.2797
Variance2.1393476
MonotonicityNot monotonic
2024-09-29T20:21:03.983479image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47007
89.7%
3.6794262 1
 
< 0.1%
3.679850036 1
 
< 0.1%
3.680054243 1
 
< 0.1%
3.680160356 1
 
< 0.1%
3.679771421 1
 
< 0.1%
3.679596645 1
 
< 0.1%
3.67996508 1
 
< 0.1%
3.679643929 1
 
< 0.1%
3.679364987 1
 
< 0.1%
Other values (1078) 1078
 
2.1%
(Missing) 4319
 
8.2%
ValueCountFrequency (%)
0 47007
89.7%
0.001175312616 1
 
< 0.1%
0.02134601408 1
 
< 0.1%
0.02238436258 1
 
< 0.1%
0.0448427214 1
 
< 0.1%
0.07605827248 1
 
< 0.1%
0.07701893377 1
 
< 0.1%
0.07844730439 1
 
< 0.1%
0.08003383286 1
 
< 0.1%
0.08020449798 1
 
< 0.1%
ValueCountFrequency (%)
16.00344099 1
< 0.1%
16.0028945 1
< 0.1%
16.0026284 1
< 0.1%
16.00194744 1
< 0.1%
16.00186885 1
< 0.1%
16.00161009 1
< 0.1%
16.00151308 1
< 0.1%
16.00149385 1
< 0.1%
16.00140251 1
< 0.1%
16.001383 1
< 0.1%

14618_FERM0101.Biocontainer_Pressure_PV
Real number (ℝ)

MISSING 

Distinct20406
Distinct (%)42.4%
Missing4317
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean180.59735
Minimum-6.8200256
Maximum480
Zeros9
Zeros (%)< 0.1%
Negative20073
Negative (%)38.3%
Memory size2.8 MiB
2024-09-29T20:21:04.058020image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-6.8200256
5-th percentile-1.9292566
Q1-0.8246521
median1.4035326
Q3480
95-th percentile480
Maximum480
Range486.82003
Interquartile range (IQR)480.82465

Descriptive statistics

Standard deviation232.65288
Coefficient of variation (CV)1.2882408
Kurtosis-1.7400277
Mean180.59735
Median Absolute Deviation (MAD)2.834654
Skewness0.50983058
Sum8686010.3
Variance54127.361
MonotonicityNot monotonic
2024-09-29T20:21:04.132432image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
480 18107
34.5%
-0.7841430664 211
 
0.4%
-1.270257568 208
 
0.4%
-1.027197266 202
 
0.4%
-0.8246520996 198
 
0.4%
-1.25 190
 
0.4%
-0.358795166 179
 
0.3%
-0.3385437012 172
 
0.3%
-1.067706299 157
 
0.3%
-0.7638916016 157
 
0.3%
Other values (20396) 28315
54.0%
(Missing) 4317
 
8.2%
ValueCountFrequency (%)
-6.820025635 1
< 0.1%
-5.553515655 1
< 0.1%
-4.358417209 1
< 0.1%
-4.358299059 1
< 0.1%
-4.348956299 1
< 0.1%
-4.325953272 1
< 0.1%
-4.316362015 1
< 0.1%
-4.315764755 1
< 0.1%
-4.308447266 1
< 0.1%
-4.298823655 1
< 0.1%
ValueCountFrequency (%)
480 18107
34.5%
8.858420452 1
 
< 0.1%
8.370230801 1
 
< 0.1%
8.001777869 1
 
< 0.1%
7.961963132 1
 
< 0.1%
7.935359641 1
 
< 0.1%
7.262870687 1
 
< 0.1%
7.238857972 1
 
< 0.1%
7.06877937 1
 
< 0.1%
6.82586372 1
 
< 0.1%

14618_FERM0101.DO_1_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct3352
Distinct (%)7.0%
Missing4319
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean2.7690941
Minimum0
Maximum102.02023
Zeros43568
Zeros (%)83.1%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:21:04.204559image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile18.402357
Maximum102.02023
Range102.02023
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.635949
Coefficient of variation (CV)3.840949
Kurtosis26.384366
Mean2.7690941
Median Absolute Deviation (MAD)0
Skewness4.9160019
Sum133176.81
Variance113.12341
MonotonicityNot monotonic
2024-09-29T20:21:04.276697image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 43568
83.1%
16.01008301 28
 
0.1%
16.15122986 26
 
< 0.1%
16.28733521 23
 
< 0.1%
15.73282928 15
 
< 0.1%
15.84533691 12
 
< 0.1%
15.5926651 9
 
< 0.1%
15.17424011 8
 
< 0.1%
16.14216461 8
 
< 0.1%
16.38938141 8
 
< 0.1%
Other values (3342) 4389
 
8.4%
(Missing) 4319
 
8.2%
ValueCountFrequency (%)
0 43568
83.1%
0.02002252489 3
 
< 0.1%
0.02044859082 1
 
< 0.1%
0.2953322411 4
 
< 0.1%
0.5756475925 4
 
< 0.1%
0.8509572983 4
 
< 0.1%
1.121261406 2
 
< 0.1%
1.125600793 1
 
< 0.1%
1.126266956 3
 
< 0.1%
1.26141901 1
 
< 0.1%
ValueCountFrequency (%)
102.0202271 1
< 0.1%
92.85025024 1
< 0.1%
92.67866821 1
< 0.1%
91.99846802 1
< 0.1%
91.96911011 1
< 0.1%
90.54976807 1
< 0.1%
90.20249023 1
< 0.1%
89.45551251 1
< 0.1%
88.00853271 1
< 0.1%
88.00588916 1
< 0.1%

14618_FERM0101.DO_2_PV
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing51162
Missing (%)97.6%
Memory size2.8 MiB
0.0
1251 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3753
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1251
 
2.4%
(Missing) 51162
97.6%

Length

2024-09-29T20:21:04.341314image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T20:21:04.389392image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1251
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2502
66.7%
. 1251
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3753
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2502
66.7%
. 1251
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3753
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2502
66.7%
. 1251
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3753
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2502
66.7%
. 1251
33.3%

14618_FERM0101.Gas_Overlay_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct25840
Distinct (%)53.7%
Missing4318
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean2.1767573
Minimum0
Maximum16.104419
Zeros22256
Zeros (%)42.5%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:21:04.443422image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.9996518
Q34.000023
95-th percentile4.000317
Maximum16.104419
Range16.104419
Interquartile range (IQR)4.000023

Descriptive statistics

Standard deviation2.0990125
Coefficient of variation (CV)0.96428413
Kurtosis1.943548
Mean2.1767573
Median Absolute Deviation (MAD)0.0007119272
Skewness0.46918134
Sum104691.14
Variance4.4058536
MonotonicityNot monotonic
2024-09-29T20:21:04.516793image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22256
42.5%
4.000057112 1
 
< 0.1%
4.000367396 1
 
< 0.1%
4.000241637 1
 
< 0.1%
3.999813923 1
 
< 0.1%
3.99977573 1
 
< 0.1%
4.000331056 1
 
< 0.1%
4.000628803 1
 
< 0.1%
4.000115933 1
 
< 0.1%
3.999896725 1
 
< 0.1%
Other values (25830) 25830
49.3%
(Missing) 4318
 
8.2%
ValueCountFrequency (%)
0 22256
42.5%
0.9544483463 1
 
< 0.1%
2.39974252 1
 
< 0.1%
2.399779091 1
 
< 0.1%
2.399879811 1
 
< 0.1%
2.399909345 1
 
< 0.1%
2.39993115 1
 
< 0.1%
2.399958561 1
 
< 0.1%
2.400025709 1
 
< 0.1%
2.400041975 1
 
< 0.1%
ValueCountFrequency (%)
16.10441948 1
< 0.1%
16.03274971 1
< 0.1%
16.02965684 1
< 0.1%
16.01333154 1
< 0.1%
16.00741039 1
< 0.1%
16.00628667 1
< 0.1%
16.00532541 1
< 0.1%
16.00509485 1
< 0.1%
16.00416077 1
< 0.1%
16.00390381 1
< 0.1%

14618_FERM0101.Load_Cell_Net_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct2263
Distinct (%)4.7%
Missing4317
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean63.01686
Minimum-9.0400002
Maximum182.56
Zeros985
Zeros (%)1.9%
Negative19324
Negative (%)36.9%
Memory size2.8 MiB
2024-09-29T20:21:04.589650image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-9.0400002
5-th percentile-6.9600006
Q1-6.2400002
median5.9200001
Q3159.12
95-th percentile167.76102
Maximum182.56
Range191.6
Interquartile range (IQR)165.36

Descriptive statistics

Standard deviation74.35043
Coefficient of variation (CV)1.1798498
Kurtosis-1.6475377
Mean63.01686
Median Absolute Deviation (MAD)13.04
Skewness0.37271496
Sum3030858.9
Variance5527.9865
MonotonicityNot monotonic
2024-09-29T20:21:04.662965image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.8 2868
 
5.5%
-6.719999695 1925
 
3.7%
-6.880000305 1672
 
3.2%
-6.640000153 1375
 
2.6%
-5.840000153 1060
 
2.0%
-7.119999695 997
 
1.9%
0 985
 
1.9%
0.07999992371 971
 
1.9%
-5.920000076 808
 
1.5%
-6.240000153 751
 
1.4%
Other values (2253) 34684
66.2%
(Missing) 4317
 
8.2%
ValueCountFrequency (%)
-9.040000153 19
 
< 0.1%
-7.519999695 3
 
< 0.1%
-7.43999939 290
0.6%
-7.42341168 1
 
< 0.1%
-7.422402028 1
 
< 0.1%
-7.421834435 1
 
< 0.1%
-7.377805421 1
 
< 0.1%
-7.377762292 1
 
< 0.1%
-7.376931486 1
 
< 0.1%
-7.376804696 1
 
< 0.1%
ValueCountFrequency (%)
182.5599976 1
< 0.1%
181.3599976 1
< 0.1%
180.9888049 1
< 0.1%
179.6800049 1
< 0.1%
179.5377841 1
< 0.1%
179.0773461 1
< 0.1%
178.5305097 1
< 0.1%
178.004149 1
< 0.1%
177.2018284 1
< 0.1%
176.6400024 1
< 0.1%

14618_FERM0101.pH_1_PV
Real number (ℝ)

MISSING 

Distinct2477
Distinct (%)5.2%
Missing4317
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean-31.45005
Minimum-527.38716
Maximum9.5703156
Zeros55
Zeros (%)0.1%
Negative3326
Negative (%)6.3%
Memory size2.8 MiB
2024-09-29T20:21:04.737906image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-527.38716
5-th percentile-527.38716
Q14.5398506
median6.2720001
Q36.2720001
95-th percentile6.2985077
Maximum9.5703156
Range536.95747
Interquartile range (IQR)1.7321495

Descriptive statistics

Standard deviation135.05524
Coefficient of variation (CV)-4.2942774
Kurtosis9.5573648
Mean-31.45005
Median Absolute Deviation (MAD)0.0066684723
Skewness-3.3991831
Sum-1512621.6
Variance18239.918
MonotonicityNot monotonic
2024-09-29T20:21:04.809277image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.272000122 23841
45.5%
4.539850616 4451
 
8.5%
-527.3871582 3315
 
6.3%
6.368000031 1433
 
2.7%
1.547423744 1298
 
2.5%
6.240000153 1164
 
2.2%
1.870640564 799
 
1.5%
1.248973465 725
 
1.4%
1.921421623 663
 
1.3%
1.725261307 534
 
1.0%
Other values (2467) 9873
18.8%
(Missing) 4317
 
8.2%
ValueCountFrequency (%)
-527.3871582 3315
6.3%
-526.3940933 1
 
< 0.1%
-526.1155903 1
 
< 0.1%
-525.0820986 1
 
< 0.1%
-516.1368823 1
 
< 0.1%
-515.2058735 1
 
< 0.1%
-191.6294922 1
 
< 0.1%
-8.040552655 1
 
< 0.1%
-6.776113243 1
 
< 0.1%
-5.625047633 1
 
< 0.1%
ValueCountFrequency (%)
9.570315552 1
< 0.1%
9.564295103 1
< 0.1%
9.553175354 1
< 0.1%
9.552114868 1
< 0.1%
9.52927475 1
< 0.1%
8.959867859 1
< 0.1%
8.941677173 1
< 0.1%
8.706869507 1
< 0.1%
8.659645779 1
< 0.1%
8.614128113 1
< 0.1%

14618_FERM0101.pH_2_PV
Real number (ℝ)

MISSING 

Distinct4443
Distinct (%)9.2%
Missing4317
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean2.5007568
Minimum-0.34243126
Maximum10.347078
Zeros18
Zeros (%)< 0.1%
Negative1501
Negative (%)2.9%
Memory size2.8 MiB
2024-09-29T20:21:04.880903image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-0.34243126
5-th percentile0.59812069
Q10.59812069
median1.4241299
Q35.734057
95-th percentile6.2683555
Maximum10.347078
Range10.689509
Interquartile range (IQR)5.1359363

Descriptive statistics

Standard deviation2.3141617
Coefficient of variation (CV)0.92538457
Kurtosis-1.0091043
Mean2.5007568
Median Absolute Deviation (MAD)0.82600918
Skewness0.84111392
Sum120276.4
Variance5.3553446
MonotonicityNot monotonic
2024-09-29T20:21:04.953843image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5981206894 12295
23.5%
1.311432457 3268
 
6.2%
1.410075378 1894
 
3.6%
1.713100052 1608
 
3.1%
1.469761086 1531
 
2.9%
0.8277111053 1426
 
2.7%
-0.3424312592 1399
 
2.7%
1.346100426 1097
 
2.1%
1.442509651 897
 
1.7%
1.268571854 754
 
1.4%
Other values (4433) 21927
41.8%
(Missing) 4317
 
8.2%
ValueCountFrequency (%)
-0.3424312592 1399
2.7%
-0.09845277007 1
 
< 0.1%
-0.09799090862 1
 
< 0.1%
-0.06752948761 100
 
0.2%
0 18
 
< 0.1%
0.001008301827 1
 
< 0.1%
0.002889567541 1
 
< 0.1%
0.004881653754 1
 
< 0.1%
0.2439928552 1
 
< 0.1%
0.2686611904 1
 
< 0.1%
ValueCountFrequency (%)
10.34707794 27
0.1%
10.33210297 1
 
< 0.1%
10.26894531 1
 
< 0.1%
10.19732208 1
 
< 0.1%
10.13416443 1
 
< 0.1%
10.06221619 1
 
< 0.1%
9.998046989 1
 
< 0.1%
9.918283655 1
 
< 0.1%
9.846372986 1
 
< 0.1%
9.783215332 1
 
< 0.1%

14618_FERM0101.PUMP_1_PV
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing4319
Missing (%)8.2%
Memory size2.8 MiB
0.0
48093 
48.0
 
1

Length

Max length4
Median length3
Mean length3.0000208
Min length3

Characters and Unicode

Total characters144283
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48093
91.8%
48.0 1
 
< 0.1%
(Missing) 4319
 
8.2%

Length

2024-09-29T20:21:05.021131image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T20:21:05.070713image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48093
> 99.9%
48.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 96187
66.7%
. 48094
33.3%
4 1
 
< 0.1%
8 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 144283
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 96187
66.7%
. 48094
33.3%
4 1
 
< 0.1%
8 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 144283
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 96187
66.7%
. 48094
33.3%
4 1
 
< 0.1%
8 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 144283
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 96187
66.7%
. 48094
33.3%
4 1
 
< 0.1%
8 1
 
< 0.1%

14618_FERM0101.PUMP_1_TOTAL
Real number (ℝ)

MISSING  ZEROS 

Distinct124
Distinct (%)0.3%
Missing4317
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean18.441754
Minimum0
Maximum277.7603
Zeros7595
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:21:05.122814image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.4399994
median12.4
Q319.840001
95-th percentile34.719998
Maximum277.7603
Range277.7603
Interquartile range (IQR)12.400002

Descriptive statistics

Standard deviation27.211317
Coefficient of variation (CV)1.4755276
Kurtosis13.09944
Mean18.441754
Median Absolute Deviation (MAD)7.4400002
Skewness3.6113309
Sum886974.59
Variance740.45579
MonotonicityNot monotonic
2024-09-29T20:21:05.191923image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7595
14.5%
9.919999695 6517
12.4%
7.43999939 5124
9.8%
24.80000153 5098
9.7%
14.88000031 4727
9.0%
12.4 3723
7.1%
4.959999847 2934
 
5.6%
19.84000092 2892
 
5.5%
17.36000061 2740
 
5.2%
138.8800415 1866
 
3.6%
Other values (114) 4880
9.3%
(Missing) 4317
8.2%
ValueCountFrequency (%)
0 7595
14.5%
0.00974709501 1
 
< 0.1%
0.03703816176 1
 
< 0.1%
0.05264017242 1
 
< 0.1%
0.05308667913 1
 
< 0.1%
0.1663549549 1
 
< 0.1%
0.1776579516 1
 
< 0.1%
0.2694939981 1
 
< 0.1%
0.4853862864 1
 
< 0.1%
0.6648702185 1
 
< 0.1%
ValueCountFrequency (%)
277.7603027 1
 
< 0.1%
138.8800415 1866
3.6%
133.9200317 53
 
0.1%
116.5599976 44
 
0.1%
108.7680082 1
 
< 0.1%
104.1599731 132
 
0.3%
101.6799683 1
 
< 0.1%
96.7199707 85
 
0.2%
86.79997559 78
 
0.1%
81.83997803 2
 
< 0.1%

14618_FERM0101.PUMP_2_PV
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct252
Distinct (%)0.5%
Missing4319
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean0.014244512
Minimum0
Maximum72
Zeros47837
Zeros (%)91.3%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:21:05.261402image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4497774
Coefficient of variation (CV)31.575487
Kurtosis16368.253
Mean0.014244512
Median Absolute Deviation (MAD)0
Skewness113.03489
Sum685.07555
Variance0.20229971
MonotonicityNot monotonic
2024-09-29T20:21:05.333867image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47837
91.3%
8 3
 
< 0.1%
0.3999938965 3
 
< 0.1%
0.799987793 2
 
< 0.1%
1.599975586 2
 
< 0.1%
3.52133918 1
 
< 0.1%
5.337225248 1
 
< 0.1%
2.799957275 1
 
< 0.1%
6.919450427 1
 
< 0.1%
5.599914551 1
 
< 0.1%
Other values (242) 242
 
0.5%
(Missing) 4319
 
8.2%
ValueCountFrequency (%)
0 47837
91.3%
8.153406573 × 10-51
 
< 0.1%
0.0003766481819 1
 
< 0.1%
0.0003819443561 1
 
< 0.1%
0.0003871383672 1
 
< 0.1%
0.0004031073601 1
 
< 0.1%
0.0004059485857 1
 
< 0.1%
0.0004249513297 1
 
< 0.1%
0.0004291392644 1
 
< 0.1%
0.0004307850753 1
 
< 0.1%
ValueCountFrequency (%)
72 1
 
< 0.1%
48 1
 
< 0.1%
8 3
< 0.1%
7.901955109 1
 
< 0.1%
7.775875092 1
 
< 0.1%
7.759765421 1
 
< 0.1%
7.479987335 1
 
< 0.1%
6.919450427 1
 
< 0.1%
6.662841346 1
 
< 0.1%
6.657589845 1
 
< 0.1%

14618_FERM0101.PUMP_2_TOTAL
Real number (ℝ)

MISSING  ZEROS 

Distinct308
Distinct (%)0.6%
Missing4318
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean27.468309
Minimum0
Maximum887.69902
Zeros25441
Zeros (%)48.5%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:21:05.405508image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q321.484485
95-th percentile80.432556
Maximum887.69902
Range887.69902
Interquartile range (IQR)21.484485

Descriptive statistics

Standard deviation88.202283
Coefficient of variation (CV)3.2110562
Kurtosis48.280865
Mean27.468309
Median Absolute Deviation (MAD)0
Skewness6.5866635
Sum1321088.3
Variance7779.6428
MonotonicityNot monotonic
2024-09-29T20:21:05.476809image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25441
48.5%
11.8683548 3770
 
7.2%
62.8109314 3386
 
6.5%
18.15523224 1624
 
3.1%
27.21213379 1440
 
2.7%
6.999950409 912
 
1.7%
15.8484024 892
 
1.7%
29.77175903 792
 
1.5%
54.69425049 775
 
1.5%
5.658672714 765
 
1.5%
Other values (298) 8298
 
15.8%
(Missing) 4318
 
8.2%
ValueCountFrequency (%)
0 25441
48.5%
0.1650124246 1
 
< 0.1%
0.7794020176 1
 
< 0.1%
0.9340991974 40
 
0.1%
0.9354771614 7
 
< 0.1%
0.938307476 6
 
< 0.1%
1.082270622 130
 
0.2%
1.319999981 89
 
0.2%
1.336016557 1
 
< 0.1%
1.367676144 1
 
< 0.1%
ValueCountFrequency (%)
887.6990234 130
 
0.2%
625.8225586 587
1.1%
613.694043 1
 
< 0.1%
609.7284668 1
 
< 0.1%
595.3473633 1
 
< 0.1%
584.563623 1
 
< 0.1%
579.7435059 1
 
< 0.1%
567.3348633 1
 
< 0.1%
561.5749512 1
 
< 0.1%
552.5736816 1
 
< 0.1%

14618_FERM0101.Single_Use_DO_PV
Real number (ℝ)

MISSING 

Distinct5043
Distinct (%)10.5%
Missing4319
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean707.84403
Minimum0
Maximum943.94336
Zeros17
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:21:05.548012image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.009894
Q1735.89551
median799.99199
Q3799.99199
95-th percentile861.68896
Maximum943.94336
Range943.94336
Interquartile range (IQR)64.096484

Descriptive statistics

Standard deviation239.38477
Coefficient of variation (CV)0.33818858
Kurtosis3.9586903
Mean707.84403
Median Absolute Deviation (MAD)0
Skewness-2.3555859
Sum34043051
Variance57305.069
MonotonicityNot monotonic
2024-09-29T20:21:05.618729image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
799.9919922 26288
50.2%
861.6889648 3318
 
6.3%
753.5009766 1753
 
3.3%
703.7227051 1557
 
3.0%
711.8633301 1437
 
2.7%
770.7051758 899
 
1.7%
943.9433594 777
 
1.5%
735.8955078 740
 
1.4%
701.4396973 704
 
1.3%
672.9973633 703
 
1.3%
Other values (5033) 9918
 
18.9%
(Missing) 4319
 
8.2%
ValueCountFrequency (%)
0 17
< 0.1%
0.175999999 1
 
< 0.1%
0.1785494304 1
 
< 0.1%
0.1795871002 1
 
< 0.1%
0.185721571 1
 
< 0.1%
0.185729005 1
 
< 0.1%
0.1874370318 1
 
< 0.1%
0.1919999957 1
 
< 0.1%
0.1936220553 1
 
< 0.1%
0.193647009 1
 
< 0.1%
ValueCountFrequency (%)
943.9433594 777
 
1.5%
916.6861328 668
 
1.3%
880.4666016 21
 
< 0.1%
861.6889648 3318
 
6.3%
856.2474609 46
 
0.1%
813.320528 1
 
< 0.1%
812.7334602 1
 
< 0.1%
809.7904297 400
 
0.8%
800.6487981 1
 
< 0.1%
799.9919922 26288
50.2%

14618_FERM0101.Single_Use_pH_PV
Real number (ℝ)

MISSING 

Distinct1155
Distinct (%)2.4%
Missing4319
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean708.24391
Minimum0.095999908
Maximum801.93599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:21:05.687860image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0.095999908
5-th percentile5.9599998
Q1799.96001
median800.13599
Q3800.304
95-th percentile800.6
Maximum801.93599
Range801.83999
Interquartile range (IQR)0.34399414

Descriptive statistics

Standard deviation254.14481
Coefficient of variation (CV)0.35883797
Kurtosis3.7679337
Mean708.24391
Median Absolute Deviation (MAD)0.16801758
Skewness-2.4014909
Sum34062283
Variance64589.583
MonotonicityNot monotonic
2024-09-29T20:21:05.763165image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
800.3040039 6179
 
11.8%
799.9600098 5179
 
9.9%
800.2799805 2325
 
4.4%
799.9919922 2195
 
4.2%
800.0080078 2128
 
4.1%
800.4319824 2069
 
3.9%
800.0799805 1919
 
3.7%
800.1199707 1896
 
3.6%
800.5759766 1616
 
3.1%
800.6959961 1610
 
3.1%
Other values (1145) 20978
40.0%
(Missing) 4319
 
8.2%
ValueCountFrequency (%)
0.09599990845 1
 
< 0.1%
0.3120002747 36
0.1%
3.06400032 1
 
< 0.1%
3.71274225 1
 
< 0.1%
4.244668042 1
 
< 0.1%
4.246134628 1
 
< 0.1%
4.246606346 1
 
< 0.1%
4.257382603 1
 
< 0.1%
4.257386492 1
 
< 0.1%
4.257560147 1
 
< 0.1%
ValueCountFrequency (%)
801.9359863 551
 
1.1%
800.6959961 1610
3.1%
800.6 466
 
0.9%
800.5759766 1616
3.1%
800.5120117 1337
2.6%
800.447998 477
 
0.9%
800.4319824 2069
3.9%
800.352002 569
 
1.1%
800.3359863 470
 
0.9%
800.3279785 476
 
0.9%

14618_FERM0101.Temperatura_PV
Real number (ℝ)

MISSING 

Distinct31607
Distinct (%)65.7%
Missing4318
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean16.607862
Minimum-0.24799805
Maximum80.968005
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size2.8 MiB
2024-09-29T20:21:05.838386image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-0.24799805
5-th percentile3.223999
Q113.656006
median16.72304
Q319.845951
95-th percentile29.55939
Maximum80.968005
Range81.216003
Interquartile range (IQR)6.189945

Descriptive statistics

Standard deviation8.1511146
Coefficient of variation (CV)0.49079854
Kurtosis-0.59839087
Mean16.607862
Median Absolute Deviation (MAD)3.1057248
Skewness-0.076160193
Sum798755.14
Variance66.440669
MonotonicityNot monotonic
2024-09-29T20:21:05.914532image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.208001709 540
 
1.0%
3.2 431
 
0.8%
3.247998047 388
 
0.7%
3.232000732 344
 
0.7%
3.255999756 212
 
0.4%
3.191998291 133
 
0.3%
3.223999023 130
 
0.2%
29.71199951 103
 
0.2%
29.71999512 98
 
0.2%
3.279998779 98
 
0.2%
Other values (31597) 45618
87.0%
(Missing) 4318
 
8.2%
ValueCountFrequency (%)
-0.2479980469 1
< 0.1%
3.160515912 1
< 0.1%
3.167999268 2
< 0.1%
3.168187587 1
< 0.1%
3.16821143 1
< 0.1%
3.171719634 1
< 0.1%
3.1722099 1
< 0.1%
3.175977906 1
< 0.1%
3.176000977 2
< 0.1%
3.176174241 1
< 0.1%
ValueCountFrequency (%)
80.96800537 1
< 0.1%
30.22399902 1
< 0.1%
30.19081118 1
< 0.1%
30.14399414 1
< 0.1%
30.00799561 1
< 0.1%
29.98349416 1
< 0.1%
29.95298909 1
< 0.1%
29.94470398 1
< 0.1%
29.92430854 1
< 0.1%
29.85929605 1
< 0.1%

Interactions

2024-09-29T20:20:59.517171image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:47.025224image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:48.049311image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:49.030408image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:49.986995image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:50.923152image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:51.913201image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:52.890166image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:53.835953image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:54.765765image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:55.712208image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:56.670021image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:57.594527image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:58.523020image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:59.589868image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:47.128325image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:48.122949image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:49.101553image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:50.057137image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:50.996791image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:51.985061image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:52.960643image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:53.905054image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:54.836748image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:55.783362image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:56.739725image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:57.662882image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:58.596653image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:59.664413image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:47.204744image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:48.195390image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:49.174145image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:50.127808image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:51.069384image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:52.059276image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:53.032352image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:53.975376image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:54.906765image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:55.855514image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:56.808851image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:57.733553image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:58.671288image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:59.732116image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:47.274621image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:48.265043image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:49.239262image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:50.192942image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:51.141351image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:52.126896image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:53.097081image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:54.040215image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:54.971982image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:55.922363image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:56.872854image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:57.798177image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:58.740805image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:59.800658image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:47.342836image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:48.332726image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:49.305394image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:50.255681image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:51.208353image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:52.194454image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:53.162219image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:54.104262image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:55.037000image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:55.987994image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:56.935362image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:57.862125image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:58.809248image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:59.878036image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:47.415985image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:48.405483image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:49.377060image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:50.325992image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:51.280126image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:52.267215image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:53.232139image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:54.175148image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:55.107165image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:56.059628image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:57.005205image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:57.931065image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:58.882218image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:59.950357image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:47.487963image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:48.477177image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:49.447187image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:50.395679image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:51.351754image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:52.337875image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:53.302259image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:54.243503image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:55.176697image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:56.130704image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:57.073384image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:58.000740image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:58.954489image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:00.019622image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:47.564955image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:48.545314image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:49.512967image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:50.460769image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:51.420445image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:52.406098image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:53.366684image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:54.307544image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:55.241840image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:56.196806image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:57.138095image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:58.066914image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:59.023550image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:00.087402image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:47.632155image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:48.612030image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:49.579144image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:50.524752image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:51.488114image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:52.473362image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:53.431109image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:54.370815image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:55.305389image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:56.263440image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:57.201838image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:58.129255image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:59.093145image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:00.154326image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:47.700368image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:48.679165image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:49.645149image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:50.589320image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:51.554817image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:52.540513image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:53.496847image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:54.434387image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:55.369057image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:56.328387image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:57.265205image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:58.193498image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:59.161791image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:00.226723image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:47.770289image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:48.750818image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:49.714898image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:50.656855image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:51.626918image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:52.612118image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:53.565938image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:54.501639image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:55.438710image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:56.397171image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:57.332460image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:58.260905image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:59.235824image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:00.292218image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:47.835410image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:48.816447image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:49.779049image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:50.719122image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:51.700844image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:52.678136image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:53.630049image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:54.563470image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:55.499957image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:56.462307image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:57.393596image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:58.321678image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:59.301831image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:00.359952image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:47.903095image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:48.883599image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:49.843704image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:50.782805image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:51.767675image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:52.744722image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:53.694144image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:54.625443image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:55.563592image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:56.526944image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:57.455042image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:58.383349image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:59.369368image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:00.433419image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:47.976907image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:48.955254image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:49.915370image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:50.853634image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:51.840360image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:52.817270image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:53.764235image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:54.697128image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:55.639199image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:56.599051image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:57.525036image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:58.452388image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:20:59.441921image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Missing values

2024-09-29T20:21:00.517562image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-29T20:21:00.666975image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-29T20:21:00.862479image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

14618_FERM0101.Agitation_PV14618_FERM0101.Air_Sparge_PV14618_FERM0101.Biocontainer_Pressure_PV14618_FERM0101.DO_1_PV14618_FERM0101.DO_2_PV14618_FERM0101.Gas_Overlay_PV14618_FERM0101.Load_Cell_Net_PV14618_FERM0101.pH_1_PV14618_FERM0101.pH_2_PV14618_FERM0101.PUMP_1_PV14618_FERM0101.PUMP_1_TOTAL14618_FERM0101.PUMP_2_PV14618_FERM0101.PUMP_2_TOTAL14618_FERM0101.Single_Use_DO_PV14618_FERM0101.Single_Use_pH_PV14618_FERM0101.Temperatura_PV
DateTime
2023-03-15 00:00:00.00072.00.01.86961215.953993NaN4.000057169.600000-527.3871585.9984780.0104.1599730.00.016.7135276.01600029.456006
2023-03-15 00:15:00.00072.00.01.95026019.056868NaN3.999801169.600000-527.3871586.0063790.0104.1599730.00.020.1470996.01600029.320005
2023-03-15 00:30:00.00072.00.01.66667322.584024NaN4.000541169.600000-527.3871586.0063790.0104.1599730.00.024.0990586.01600029.720002
2023-03-15 00:45:00.00072.00.01.68686513.837698NaN3.999813169.600000-527.3871586.0063790.0104.1599730.00.014.2184146.01597829.272062
2023-03-15 01:00:00.00072.00.01.70694816.378275NaN3.999787169.600000-527.3871586.0063790.0104.1599730.00.017.1157246.01600029.472092
2023-03-15 01:15:00.00072.00.01.91013818.632587NaN3.999956169.600000-527.3871586.0063790.0104.1599730.00.019.8491746.01600029.591338
2023-03-15 01:30:00.00072.00.01.91009821.592331NaN3.999718169.600000-527.3871586.0063790.0104.1599730.00.022.7613806.01600029.295996
2023-03-15 01:45:00.00072.00.02.27540822.299596NaN3.999149169.520007-527.3871586.0063790.0104.1599730.00.025.6408616.01600029.671997
2023-03-15 02:00:00.00072.00.01.82988913.979342NaN4.000402169.600000-527.3871586.0063790.0104.1599730.00.014.2928966.00800029.343825
2023-03-15 02:15:00.00072.00.01.86921416.659424NaN4.000057169.520007-527.3871586.0063790.0104.1599730.00.017.4434406.01600029.528170
14618_FERM0101.Agitation_PV14618_FERM0101.Air_Sparge_PV14618_FERM0101.Biocontainer_Pressure_PV14618_FERM0101.DO_1_PV14618_FERM0101.DO_2_PV14618_FERM0101.Gas_Overlay_PV14618_FERM0101.Load_Cell_Net_PV14618_FERM0101.pH_1_PV14618_FERM0101.pH_2_PV14618_FERM0101.PUMP_1_PV14618_FERM0101.PUMP_1_TOTAL14618_FERM0101.PUMP_2_PV14618_FERM0101.PUMP_2_TOTAL14618_FERM0101.Single_Use_DO_PV14618_FERM0101.Single_Use_pH_PV14618_FERM0101.Temperatura_PV
DateTime
2024-09-10 21:45:00.0000.00.0480.00.0NaN0.0-6.241.248973-0.3424310.029.7600010.00.0735.895508800.0399915.370399
2024-09-10 22:00:00.0000.00.0480.00.0NaN0.0-6.241.248973-0.3424310.029.7600010.00.0735.895508800.0399915.272946
2024-09-10 22:15:00.0000.00.0480.00.00.00.0-6.241.248973-0.3424310.029.7600010.00.0735.895508800.0399915.394324
2024-09-10 22:30:00.0000.00.0480.00.0NaN0.0-6.241.248973-0.3424310.029.7600010.00.0735.895508800.0399915.275258
2024-09-10 22:45:00.0000.00.0480.00.0NaN0.0-6.241.248973-0.3424310.029.7600010.00.0735.895508800.0399915.231995
2024-09-10 23:00:00.0000.00.0480.00.0NaN0.0-6.241.248973-0.3424310.029.7600010.00.0735.895508800.0399915.353003
2024-09-10 23:15:00.0000.00.0480.00.0NaN0.0-6.241.248973-0.3424310.029.7600010.00.0735.895508800.0399915.251117
2024-09-10 23:30:00.0000.00.0480.00.0NaN0.0-6.241.248973-0.3424310.029.7600010.00.0735.895508800.0399915.239383
2024-09-10 23:45:00.0000.00.0480.00.0NaN0.0-6.241.248973-0.3424310.029.7600010.00.0735.895508800.0399915.224738
2024-09-11 00:00:00.0000.00.0480.00.0NaN0.0-6.241.248973-0.3424310.029.7600010.00.0735.895508800.0399915.213614

Duplicate rows

Most frequently occurring

14618_FERM0101.Agitation_PV14618_FERM0101.Air_Sparge_PV14618_FERM0101.Biocontainer_Pressure_PV14618_FERM0101.DO_1_PV14618_FERM0101.DO_2_PV14618_FERM0101.Gas_Overlay_PV14618_FERM0101.Load_Cell_Net_PV14618_FERM0101.pH_1_PV14618_FERM0101.pH_2_PV14618_FERM0101.PUMP_1_PV14618_FERM0101.PUMP_1_TOTAL14618_FERM0101.PUMP_2_PV14618_FERM0101.PUMP_2_TOTAL14618_FERM0101.Single_Use_DO_PV14618_FERM0101.Single_Use_pH_PV14618_FERM0101.Temperatura_PV# duplicates
1370NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4317
5850.00.0480.00.0NaN0.0-6.806.2721.3114320.024.8000020.062.810931861.688965800.30400426.20000017
5860.00.0480.00.0NaN0.0-6.806.2721.3114320.024.8000020.062.810931861.688965800.30400426.21600315
2780.00.0480.00.0NaN0.0-6.886.2721.3114320.024.8000020.062.810931861.688965800.30400426.78399713
2870.00.0480.00.0NaN0.0-6.886.2721.3114320.024.8000020.062.810931861.688965800.30400426.92800313
2880.00.0480.00.0NaN0.0-6.886.2721.3114320.024.8000020.062.810931861.688965800.30400426.94399413
3110.00.0480.00.0NaN0.0-6.886.2721.3114320.024.8000020.062.810931861.688965800.30400427.31200013
3150.00.0480.00.0NaN0.0-6.886.2721.3114320.024.8000020.062.810931861.688965800.30400427.36800513
2670.00.0480.00.0NaN0.0-6.886.2721.3114320.024.8000020.062.810931861.688965800.30400426.60799612
2700.00.0480.00.0NaN0.0-6.886.2721.3114320.024.8000020.062.810931861.688965800.30400426.65600612